Make unimplemented dataloader hooks raise `NotImplementedError` (#9161)

CHANGELOG.md

@@ -143,6 +143,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - `Trainer.request_dataloader` now takes a `RunningStage` enum instance ([#8858](https://github.com/PyTorchLightning/pytorch-lightning/pull/8858))
 
 
+- Changed `rank_zero_warn` to `NotImplementedError` in the `{train, val, test, predict}_dataloader` hooks that `Lightning(Data)Module` uses ([#9161](https://github.com/PyTorchLightning/pytorch-lightning/pull/9161))
+
 ### Deprecated
 
 - Deprecated `LightningModule.summarize()` in favor of `pytorch_lightning.utilities.model_summary.summarize()`

pytorch_lightning/core/hooks.py

@@ -18,7 +18,7 @@ from typing import Any, Dict, List, Optional
 import torch
 from torch.optim.optimizer import Optimizer
 
-from pytorch_lightning.utilities import move_data_to_device, rank_zero_warn
+from pytorch_lightning.utilities import move_data_to_device
 from pytorch_lightning.utilities.types import EVAL_DATALOADERS, STEP_OUTPUT, TRAIN_DATALOADERS
 
 
@@ -540,7 +540,7 @@ class DataHooks:
                 return {'mnist': mnist_loader, 'cifar': cifar_loader}
 
         """
-        rank_zero_warn("`train_dataloader` must be implemented to be used with the Lightning Trainer")
+        raise NotImplementedError("`train_dataloader` must be implemented to be used with the Lightning Trainer")
 
     def test_dataloader(self) -> EVAL_DATALOADERS:
         r"""
@@ -602,6 +602,7 @@ class DataHooks:
             In the case where you return multiple test dataloaders, the :meth:`test_step`
             will have an argument ``dataloader_idx`` which matches the order here.
         """
+        raise NotImplementedError("`test_dataloader` must be implemented to be used with the Lightning Trainer")
 
     def val_dataloader(self) -> EVAL_DATALOADERS:
         r"""
@@ -654,6 +655,7 @@ class DataHooks:
             In the case where you return multiple validation dataloaders, the :meth:`validation_step`
             will have an argument ``dataloader_idx`` which matches the order here.
         """
+        raise NotImplementedError("`val_dataloader` must be implemented to be used with the Lightning Trainer")
 
     def predict_dataloader(self) -> EVAL_DATALOADERS:
         r"""
@@ -679,6 +681,7 @@ class DataHooks:
             In the case where you return multiple prediction dataloaders, the :meth:`predict`
             will have an argument ``dataloader_idx`` which matches the order here.
         """
+        raise NotImplementedError("`predict_dataloader` must be implemented to be used with the Lightning Trainer")
 
     def on_train_dataloader(self) -> None:
         """Called before requesting the train dataloader."""

tests/core/test_datamodules.py

@@ -480,8 +480,10 @@ def test_dm_init_from_datasets_dataloaders(iterable):
     with mock.patch("pytorch_lightning.core.datamodule.DataLoader") as dl_mock:
         dm.train_dataloader()
         dl_mock.assert_called_once_with(train_ds, batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True)
-    assert dm.val_dataloader() is None
-    assert dm.test_dataloader() is None
+    with pytest.raises(NotImplementedError):
+        _ = dm.val_dataloader()
+    with pytest.raises(NotImplementedError):
+        _ = dm.test_dataloader()
 
     train_ds_sequence = [ds(), ds()]
     dm = LightningDataModule.from_datasets(train_ds_sequence, batch_size=4, num_workers=0)
@@ -493,8 +495,10 @@ def test_dm_init_from_datasets_dataloaders(iterable):
                 call(train_ds_sequence[1], batch_size=4, shuffle=not iterable, num_workers=0, pin_memory=True),
             ]
         )
-    assert dm.val_dataloader() is None
-    assert dm.test_dataloader() is None
+    with pytest.raises(NotImplementedError):
+        _ = dm.val_dataloader()
+    with pytest.raises(NotImplementedError):
+        _ = dm.test_dataloader()
 
     valid_ds = ds()
     test_ds = ds()
@@ -504,7 +508,8 @@ def test_dm_init_from_datasets_dataloaders(iterable):
         dl_mock.assert_called_with(valid_ds, batch_size=2, shuffle=False, num_workers=0, pin_memory=True)
         dm.test_dataloader()
         dl_mock.assert_called_with(test_ds, batch_size=2, shuffle=False, num_workers=0, pin_memory=True)
-    assert dm.train_dataloader() is None
+    with pytest.raises(NotImplementedError):
+        _ = dm.train_dataloader()
 
     valid_dss = [ds(), ds()]
     test_dss = [ds(), ds()]